Omnichannel Brand Sentiment Pulse
Track one brand keyword across every major network and roll it into a single share-of-positive scoreboard
https://apidirect.io/mcp?token=YOUR_API_KEY
Build me an omnichannel sentiment pulse for {brand} and scope the TikTok read to {region}
omnichannel-brand-sentiment-pulse.
Any agent can also call get_skill(skill_id="omnichannel-brand-sentiment-pulse") to pull these steps on demand.
What it does
Each network has a different mood; running the same query with get_sentiment everywhere and normalizing the polarity splits turns scattered chatter into one comparable, channel-by-channel reputation score.
Who it's for: Brand and comms leads who need one number for how a brand is perceived
Inputs
| Input | Required | Description | Example |
|---|---|---|---|
brand |
Yes | Brand or product name to monitor | Notion |
region |
No | 2-letter country code to scope TikTok results | us |
How your agent runs it
-
1
search_twitter(query={brand}, pages=10, sort_by=most_recent, get_sentiment=true)Tally positive/negative/neutral polarity and the dominant emotion for the X channel.
-
2
search_reddit(query={brand}, page=5, sort_by=top, get_sentiment=true)Capture the Reddit polarity split and pull the most-upvoted complaint threads.
-
3
search_youtube(query={brand}, pages=3, upload_date=this_month, get_sentiment=true)Score this month's video coverage sentiment and note the loudest creators.
-
4
search_tiktok(query={brand}, pages=3, region={region}, publish_time=30, sort_by=most_liked, get_sentiment=true)Gauge short-video buzz and the dominant emotion on the most-liked recent clips.
-
5
search_forums(query={brand}, time=month, get_sentiment=true)Add niche-community sentiment, then normalize all five channels into one weighted share-of-positive scoreboard.
Tools used
The full playbook
This is exactly what the MCP returns to your agent (via the omnichannel-brand-sentiment-pulse prompt or get_skill tool), with your inputs filled in.
SKILL: Omnichannel Brand Sentiment Pulse
Each network has a different mood; running the same query with get_sentiment everywhere and normalizing the polarity splits turns scattered chatter into one comparable, channel-by-channel reputation score.
You are running this skill on API Direct via its MCP tools. Execute the steps below yourself by calling the named tools in order — values in <angle brackets> come from a previous step. Then deliver the result described at the end.
INPUTS:
- brand (required): <brand — ASK THE USER>
- region (optional): (optional — e.g. us)
STEPS:
1. Tool `search_twitter` — search_twitter(query=<brand>, pages=10, sort_by=most_recent, get_sentiment=true)
Tally positive/negative/neutral polarity and the dominant emotion for the X channel.
2. Tool `search_reddit` — search_reddit(query=<brand>, page=5, sort_by=top, get_sentiment=true)
Capture the Reddit polarity split and pull the most-upvoted complaint threads.
3. Tool `search_youtube` — search_youtube(query=<brand>, pages=3, upload_date=this_month, get_sentiment=true)
Score this month's video coverage sentiment and note the loudest creators.
4. Tool `search_tiktok` — search_tiktok(query=<brand>, pages=3, region=<region>, publish_time=30, sort_by=most_liked, get_sentiment=true)
Gauge short-video buzz and the dominant emotion on the most-liked recent clips.
5. Tool `search_forums` — search_forums(query=<brand>, time=month, get_sentiment=true)
Add niche-community sentiment, then normalize all five channels into one weighted share-of-positive scoreboard.
DELIVER: A single-page scoreboard showing share-of-positive, dominant emotion, and top quotes for the brand on each network, plus a blended cross-channel sentiment score
Note: each underlying tool call is billed at its normal endpoint price; get_sentiment adds a small per-page surcharge. Page through results as needed but stop once you have enough to deliver the outcome.